Oil and gas well test analysis method based on production data

Abstract

The invention discloses an oil and gas well test analysis method based on production data. The method comprises the following steps that a normalized pressure equation is established according to a variable yield convolution formula; acquiring field long-term production data; performing fast deconvolution calculation on the long-term production data based on the normalized pressure equation to obtain a pressure response function and a logarithmic time derivative thereof, and preliminarily identifying a feature flow section according to the pressure response function and the logarithmic time derivative thereof; establishing an accurate identification model of the characteristic flow section based on a normalized pressure equation, performing accurate identification on the characteristic flow section of the long-term production data according to the accurate identification model, and performing parameter inversion by using the characteristic flow section to obtain stratum parameters or dynamic reserves; based on the equation, pressure and pressure derivative rapid convolution inversion based on production data can be realized, so that a traditional PTA method and a traditional RTA method are unified to form a new production data well testing analysis method.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas development, and particularly relates to a well test analysis method for oil and gas wells based on production data. Background Art

[0002] In the petroleum industry, unsteady state pressure analysis (PTA) and unsteady state rate analysis (RTA) are two core technologies for production data analysis. Through the analysis of rate and pressure data, the PTA and RTA methods can obtain reservoir parameters and dynamic reserves of oil and gas wells.

[0003] Among them, PTA is mainly used for the analysis of short-term rate and pressure test data, such as pressure drawdown data and pressure build-up data. These data points are dense and the data accuracy is high. The data is often measured in seconds, but the duration is short, and the main time unit is often hours. During the long-term production process of oil and gas wells, the wellhead pressure data needs to be converted into bottom-hole flowing pressure data, and due to the influence of wellhead measurement errors, the bottom-hole pressure and wellhead rate used in the calculation often have large noise, thus limiting the application of PTA in long-term production data analysis.

[0004] The birth of RTA aims to make up for the deficiencies of PTA in long-term production data analysis. RTA is mainly used for the analysis of long-term production data of oil and gas wells. The data sources are wellhead pressure and rate data of oil and gas wells. The data volume is large and the duration is long. The main time units are often months and years. In the RTA theoretical system, the material balance time proposed by Blasingame plays a key role. Through the introduction of the material balance time, the conversion from variable-rate production to constant-rate production is realized. Looking at the development of RTA over the past few decades, a large number of scholars have also found the following problems in the application of RTA:

[0005] (1) The material balance time lacks a clear physical meaning. Under fluctuating production rates, the material balance time will jump back and forth, losing the time series characteristics, and even showing phenomena that violate physical laws. At the same time, the change in production rate will cause the characteristic flow section in the material balance time coordinate system to show "advance" or "lag" phenomena. For example, the unsteady flow period is shown as a pseudo-steady flow section. Therefore, the theoretical chart with the material balance time as the abscissa lacks physical meaning.

[0006] (2) The theoretical system established by Blasingame is only strictly valid in the pseudo-steady state stage, and there is no strict theoretical proof in the unsteady flow stage so far, resulting in a lack of strict theoretical support for RTA. Especially for unconventional reservoirs, oil and gas wells are in the unsteady flow stage for a long time, and RTA cannot be used. At present, RTA is widely misused in the field for unconventional reservoirs.

[0007] (3) In the RTA theoretical chart, the ordinate uses the normalized pressure, and the abscissa is the material balance time, rather than the time-pressure and pressure derivative in the traditional well test chart. Therefore, the chart cannot be used for accurate identification of the characteristic flow segment, which limits the application of this method.

[0008] In summary, in the production data analysis, the RTA method lacks a strict theoretical basis in the unstable flow stage, cannot obtain the pressure and pressure derivative curves, and is difficult to accurately identify the characteristic flow segment, so it is not suitable for the production evaluation of unconventional oil and gas reservoirs. Although the PTA theory is complete, it is mainly used for short-term low-noise test data analysis and cannot be used for long-term production data analysis with large noise. Summary of the Invention

[0009] The purpose of the present invention is to provide a well test analysis method for oil and gas wells based on production data to solve the technical problems that the RTA method and the PTA method in the prior art have their respective defects and cannot meet the needs of production data analysis.

[0010] To solve the above technical problems, the present invention specifically provides the following technical solutions:

[0011] A well test analysis method for oil and gas wells based on production data includes the following steps:

[0012] Define the variable production convolution formula, and establish a normalized pressure equation based on the variable production convolution formula to explicitly represent the relationship between the normalized pressure, time variable, derivative of the pressure response function, and material balance time;

[0013] Obtain the long-term production data on site;

[0014] Perform fast deconvolution calculation on the long-term production data based on the normalized pressure equation to obtain the pressure response function and its logarithmic time derivative, and preliminarily identify the characteristic flow segment according to the pressure response function and its logarithmic time derivative;

[0015] Establish an accurate identification model for the characteristic flow segment based on the normalized pressure equation, accurately identify the characteristic flow segment of the long-term production data according to the accurate identification model, and then use the characteristic flow segment for parameter inversion to obtain formation parameters or dynamic reserves.

[0016] The present invention has the following beneficial effects compared with the prior art:

[0017] Starting from the convolution basic formula under variable production / variable pressure, the present invention establishes an explicit normalized pressure equation applicable to the entire flow stage. Based on this equation, fast convolution deconvolution of pressure and pressure derivative based on production data can be realized, thus unifying the traditional PTA method and RTA method to form a new well test analysis method for production data.

[0018] This analysis method overcomes the defects of existing PTA methods and RTA methods, can be used for both short-term test data and long-term production data analysis, and can very conveniently perform fast deconvolution calculations based on the explicit normalized pressure equation to obtain pressure and pressure derivative values, making it applicable to the characteristic flow section analysis of all flow sections, and organically combining the advantages of RTA and PTA models. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained based on the provided drawings.

[0020] Figure 1 It is a schematic flowchart provided for an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0022] As Figure 1 shown, the present invention provides a well test analysis method for oil and gas wells based on production data. First, the concept of production well test is defined: a method for identifying characteristic flow sections and obtaining reservoir parameters based on long-term production data of oil and gas wells, such as daily production data, by using well test interpretation charts (time-pressure and time-pressure logarithm time derivative).

[0023] Specifically, it includes the following steps.

[0024] (1) Establishment of theoretical model:

[0025] Starting from the basic variable production convolution formula, an explicit normalized pressure formula is established. This formula can explicitly represent the normalized pressure equation that shows the relationship between the normalized pressure, time variable, derivative of the pressure response function, and material balance time.

[0026] (2) Data acquisition:

[0027] Long-term production data: mainly includes production and pressure data during the continuous production process of oil and gas wells, with time in days.

[0028] (3) Fast deconvolution calculation:

[0029] By using the established normalized pressure formula, perform fast deconvolution calculations on production data to obtain the pressure response function p u and its logarithmic time derivative dp u / dlnt.

[0030] (4) Conduct characteristic flow segment analysis:

[0031] Based on the pressure response function and its logarithmic time derivative, initially identify the characteristic flow segments. Based on the normalized pressure equation, establish an accurate identification model for the characteristic flow segments to accurately identify the flow segments of the production data.

[0032] (5) Obtain formation parameters or dynamic reserves.

[0033] Using the characteristic flow segments, adopt the well test analysis method and steps to perform parameter inversion.

[0034] In the present invention, the traditional PTA method is used for pressure analysis of short-term test data, and the traditional RTA method is used for production and pressure analysis of long-term production data. This method unifies the traditional RTA and PTA and can be used for both short-term test data and long-term production data analysis.

[0035] Secondly, compared with test data, the biggest feature of production data is its low accuracy and large measurement errors, resulting in large data noise. Although traditional deconvolution methods have been explored for decades and are occasionally used in short-term test data, no mature algorithm that can be used for on-site actual production data has been found. Based on the proposed explicit normalized pressure model, it is very convenient to achieve fast deconvolution calculations and obtain pressure and pressure derivative values.

[0036] Furthermore, the PTA model has a strict theoretical basis. Its greatest advantage is that it can identify characteristic flow segments, but currently it can only be used for short-term test data analysis and cannot be used for production data analysis, which limits its application. RTA is theoretically incomplete. Strictly speaking, it can only be used for pseudo-steady state flow segment analysis and cannot obtain pressure and pressure derivative curves, and there are flaws in the identification of characteristic flow segments. The theoretical model proposed in the present invention is based on strict mathematical derivations, is applicable to all flow segments, and can be used for characteristic flow segment analysis, organically combining the advantages of the RTA and PTA models.

[0037] Finally, the concept of well test analysis for production data is proposed, applying the traditional short-term test well test analysis method to long-term production data analysis, so that the entire set of mature characteristic flow segment identification and parameter inversion in well test analysis can be borrowed.

[0038] The following will be described in conjunction with specific embodiments.

[0039] The present invention provides a new explicit calculation method for the convolution equation, which enables the convolution equation to be explicitly represented by a function, can intuitively display the relationships between various functions, and can greatly improve the operation efficiency of convolution and deconvolution, and can be widely applied to fast calculations under a large amount of data.

[0040] Mathematically, convolution is defined as a mathematical operator that takes two functions h and g and then generates a third function y, which represents the amount of overlap between g and the reversed form of h. In other words, convolution is defined as the integral of the product of one function reversed and shifted with the other function.

[0041] Its expression is:

[0042]

[0043] where h(t) = df(t) / dt (2);

[0044] Substituting equation (2) into equation (1) gives:

[0045]

[0046] Performing integration by parts on equation (3) gives

[0047]

[0048] In the formula:

[0049]

[0050] Equation (4) can be further expressed as:

[0051]

[0052] where y(t) is the response of the system, i.e., the convolution of the functions f(t) and g(t).

[0053] Define the convolution time function as:

[0054]

[0055] And the convolution time function and its derivative satisfy:

[0056] Γ(0) = 0, Γ'(0) = 1 (8);

[0057] Substituting equation (7) into equation (6) gives:

[0058]

[0059] Performing integration by parts on equation (9) gives:

[0060]

[0061] Integrate the equation (10) by parts again and use the initial condition equation (8) to obtain:

[0062]

[0063] In practical applications, according to the variation characteristics of the g(t) function, an appropriate convolution time function can be selected, such as a linear function, a quadratic function, an exponential function, a polynomial, etc., to further simplify equation (11).

[0064] When using a linear convolution time function, the convolution time function satisfies:

[0065] Γ(τ) = τ (12)

[0066] Substitute equation (12) into equation (11) to obtain a simplified explicit convolution equation:

[0067]

[0068] The method proposed by the present invention is based on the convolution integral equation. Through strict mathematical derivation, a general convolution expression is obtained. On this basis, a simplified explicit convolution expression under the assumption of a linear convolution time function is derived. By analogy, when other functions are selected for the convolution time function, such as a quadratic function, an exponential function, a polynomial, etc., other types of simplified explicit convolution expressions can also be obtained. For other fields that satisfy the convolution integral equation (1), the general convolution expression (equation 11) and the simplified explicit convolution expression (equation 13) proposed in this patent are still applicable, and only three functions, namely y(t), g(t), and f(t), need to be determined according to the process of this patent.

[0069] In oil industry production, the observed pressure drop is the convolution of the input flow rate function and the derivative of the constant flow rate pressure response. In the prior art, at the initial moment, assuming that the system is in an equilibrium state, the pressure drop corresponding to the t moment can be expressed as:

[0070]

[0071] Its discrete form is:

[0072]

[0073] In the formula, p u is the pressure response function, q w is the oil and gas well production rate. When the production rate q w and the pressure response function p u are known, the bottom hole pressure drop Δp w can be calculated forward through the above equation; when the production rate qw and the bottom-hole pressure drop Δp w When they are known, the pressure response function p is obtained through inverse calculation by the above equation u .

[0074] However, there are the following two problems with the above equation in engineering applications:

[0075] First, calculating the pressure drop at time t requires multiplication and addition operations, and the calculation needs to use all the discrete segments of the flow rate before time t. Therefore, the computational cost consumed is proportional to the number of discrete segments. With the advent of the big data era, especially in recent years with the application and popularization of intelligent production systems, if the production data is relatively intensive, the amount of production data is getting larger and larger, and the time required for dynamic prediction calculation by the convolution method is longer.

[0076] Second, when performing deconvolution operations using the above equation, the data errors and calculation errors before time t will accumulate; therefore, in addition to the problem of large computational volume, there are also stability problems in performing deconvolution operations.

[0077] To solve the above problems, the explicit convolution expression is adopted in the present invention. This equation requires fewer parameters and less computational volume, can greatly improve the operation efficiency of convolution and deconvolution, and can be widely applied to rapid calculations under a huge amount of data.

[0078] It is assumed that the bottom-hole pressure of single-phase and slightly compressible fluid seepage satisfies the superposition principle, and the bottom-hole pressure drop satisfies

[0079]

[0080] In the formula, t is the time variable; τ is the integration variable; p i is the initial reservoir pressure; Δp w is the bottom-hole pressure drop; p w is the bottom-hole pressure.

[0081] According to the above equation and equation (3), it can be known that:

[0082] y(t) = Δp w (t) = p i -p w (t);

[0083] g(t) = q w (t);

[0084] f(t) = p u (t);

[0085] According to the calculation equation of the cumulative production G(t) at time t described above, and defining the material balance time t mb , where:

[0086]

[0087] Substituting the above equation into the simplified explicit convolution expression (Equation 13) gives:

[0088]

[0089] The above equation is a simplified explicit pressure response convolution equation for oil production. The calculation efficiency can be greatly improved through this equation. Among them, the above equation intuitively reveals the relationship between the pressure difference, production rate, pressure response function and its derivative. Through this equation, forward convolution and inverse convolution can be realized. The equation is further rearranged to obtain the normalized pressure equation:

[0090]

[0091] In this embodiment, inverse convolution is taken as an example for illustration.

[0092] Inverse convolution: Invert the reservoir pressure response function (p w ) according to the production rate (q w ) and the pressure drop (Δp u ).

[0093] In the inverse convolution calculation, the production rate and the bottom-hole pressure drop are used as known parameters, and the pressure response function is obtained by inversion. The calculation process is as follows:

[0094] (1) Collect production well data; collect production data q w and bottom-hole flowing pressure data p wf . According to the original formation pressure p i , calculate the production pressure difference Δp w = p i - p wf .

[0095] (2) Calculate the cumulative production G q at a certain moment, i = 1, 2,..., N

[0096]

[0097] (3) Calculate the material balance time t mb ;

[0098] (4) Calculate Δp w / q w . Assuming that the pressure response function near time t can be linearly approximated, use the following formula to approximately calculate the derivative of the pressure response function dp u / dt and the logarithmic time derivative dp u / d lnt

[0099]

[0100] (5) With t, △p w / q w , dp u / dt and t mb Substitute into the following formula to calculate the pressure response function p u ;

[0101]

[0102] The comprehensive analysis for accurate identification of characteristic flow segments based on long-term production data includes the following three steps:

[0103] (1) Initially identify the characteristic flow segment according to the pressure response function and its logarithmic time derivative

[0104] Under the condition of constant production, the pressure response function p at time t of the characteristic flow segment u (t) is a linear function of the power function of the time variable t, expressed as:

[0105] p u (t) = a u + b u ·t n ;

[0106] In the formula, a u and b u are characteristic flow constants; n is the characteristic flow index;

[0107] Specifically, the pressure response function of radial flow (n = 0) is the following expression:

[0108] p u (t) = a u + b u ·ln t

[0109] The logarithmic time derivative of the characteristic flow segment is Then:

[0110]

[0111] In the double logarithmic coordinate, the logarithmic time derivative showing a straight line with a slope of n is the characteristic flow segment.

[0112] Plot the t-p u and double logarithmic chart, and obtain the slope n through piecewise fitting of the chart morphology to initially identify the possible characteristic flow segments.

[0113] (2) Accurate identification of the characteristic flow segment

[0114] Analyze the possible characteristic flow segments to establish an accurate identification model for the characteristic flow segments.

[0115] When the production rate changes relatively slowly, establish the t mbe -RNP equations for the characteristic flow segments initially recognized in step (1) as follows:

[0116] RNP(t) = a u + b u ·t mbe ;

[0117] where t mbe is the effective material balance time, and:

[0118]

[0119] Given the characteristic flow index as n, use the moving window method to calculate the characteristic flow constants a u and b u of the curve with the time variable t, and at the same time view the straight-line segment of the t mbe -RNP equation and the constant segments of the curves of the characteristic flow parameters a u and b u with the time variable t to determine whether the specified characteristic flow segment appears and the duration range (t min , t max ) of the characteristic flow segment. Within the time range (t min , t max ), when the t mbe -RNP curve shows a linear relationship, and the t-a u and t-b u curves are constants within this time period, it is considered that there is a characteristic flow segment within this time range, and its characteristic flow index is n.

[0120] (3) Reconstruct the characteristic flow segment curve and invert the reservoir parameters.

[0121] Within the time range (t min , t max ), according to the characteristic flow parameters a u and b u , use the following formula to reconstruct the characteristic flow segment curve, and the pressure response function and logarithmic time derivative of the characteristic flow segment are respectively:

[0122]

[0123] After the implementation of (1)-(3) to complete the preliminary analysis, precise identification, and reconstruction of the characteristic flow section, a conventional well test analysis model can be used for parameter inversion. For example, during pseudo-steady-state flow analysis, it is necessary to determine whether the production has entered the boundary flow stage and calculate the original geological reserves; the constructed smooth pressure response function and derivative curve can be directly used for type curve fitting, which can greatly improve the type curve fitting effect.

[0124] It should be noted that when constructing the characteristic flow, it is not necessary to accurately obtain the complete characteristic flow section, and only a section with relatively significant characteristics needs to be determined.

[0125] In the process of equation derivation of the method proposed by the present invention, no assumptions are made about the pressure response function. Therefore, the general equation proposed by the present invention can be used for convolution forward and inverse modeling in different production stages under any reservoir and production well combination. The differences in calculations for different reservoir and production well combinations are mainly reflected in the different pressure response functions. It is only necessary to select the corresponding pressure response function according to a specific reservoir and production well combination and perform calculations according to the aforementioned process.

[0126] The method proposed by the present invention is derived based on the wellbore convolution integral equation. By replacing pressure with the pseudo-pressure in the gas reservoir, this method can be extended and applied to gas reservoir calculations.

[0127] The concept of well test analysis of production data proposed by the present invention applies the traditional short-term test well test analysis method to long-term production data analysis, so that the entire set of mature characteristic flow section identification and parameter inversion in well test analysis can be borrowed.

[0128] The above embodiments are only exemplary embodiments of the present application and are not used to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions within the essence and protection scope of the present application, and such modifications or equivalent substitutions should also be regarded as falling within the protection scope of the present application.

Claims

1. A method for analyzing oil and gas well testing based on production data, characterized in that: The steps include: Define the variable yield convolution formula, and establish the normalized pressure equation that explicitly represents the normalized pressure, time variable, the derivative of the pressure response function and the material balance time relationship based on the variable yield convolution formula; Obtain long-term production data on site; Based on the normalized pressure equation, a fast deconvolution calculation is performed on the long-term production data to obtain the pressure response function and its logarithmic time derivative, and the characteristic flow section is preliminarily identified based on the pressure response function and its logarithmic time derivative; An accurate identification model of the characteristic flow segment is established based on the normalized pressure equation, and the characteristic flow segment of long-term production data is accurately identified according to the accurate identification model, and then the characteristic flow segment is used for parameter inversion to obtain formation parameters or dynamic reserves.

2. The oil and gas well testing analysis method based on production data according to claim 1, characterized in that: Assume that the two variable functions are h(t) and g(t), and generate a third function y(t) by receiving the two variable functions h(t) and g(t) through convolution. Then the convolution formula of the two variable functions h(t) and g(t) is defined as: Among them, t is the time variable; τ is the integral variable.

3. The oil and gas well testing analysis method based on production data according to claim 2, characterized in that: The normalized pressure equation is established as follows: Define the convolution time function as Γ(τ), then: In the formula, G q (t) is the cumulative output at time t; G q (t-τ) is the cumulative output at time t-τ; q w (t) is the output at time t; And the convolution time function and its derivative satisfy Γ(τ)=0, Γ'(τ)=1; Assume that the bottom hole pressure of the single-phase, slightly compressible fluid seepage satisfies the superposition principle, where the pressure drop Δp at the bottom hole at time t is w (t)Satisfy: In the formula, t is the time variable; τ is the integral variable; p i is the initial pressure; p w (t) is the bottom hole pressure; q w (t-τ) is the output at time t-τ; p u is the pressure response function, defined as the pressure drop per unit of production; When the production changes relatively slowly, the production curve shape is set to be approximated by a linear function. Then, the convolution time function satisfies Γ(τ) = τ, and the normalized pressure equation of explicit convolution is obtained: Where RNP is the abbreviation of normalized pressure, Δp w (t) is the pressure drop at time t; q w (t) is the output at time t; p u (t) is the pressure response function at time t; p u is the pressure response function; t mb is the material balance time, and t is the time variable.

4. The oil and gas well testing analysis method based on production data according to claim 3 is characterized in that: Convolution time functions include linear functions, quadratic functions, exponential functions, and polynomial functions; Among them, when Γ(τ) = τ, it is a linear convolution time function.

5. The oil and gas well testing analysis method based on production data according to claim 4, characterized in that: Based on the normalized pressure equation, the long-term production data is quickly deconvolved to obtain the pressure response function in the following way: Based on the long-term production data obtained, including the output q at time t w (t), bottom hole pressure p wf , initial pressure p i , calculate the production pressure difference Δp w =p i -p wf , and calculate the normalized pressure Δp w / q w (t); Calculate the cumulative output G at time t q (t), then: Where i = 1, 2, ..., N, where N is the number of production data points up to time t; q i is the output corresponding to the i-th data point; Calculate the material balance time t corresponding to time t mb : Assume that the pressure response function at time t is approximated by a linear function, and calculate the time derivative of the pressure response function and the logarithmic time derivative of the pressure response function: In the formula, is the derivative of the pressure response function with respect to the production time, and then the logarithmic time derivative of the pressure response function can be obtained: Calculate the pressure response function p at time t u (t): The time variable t and output q w and bottom hole pressure data p wf Substitute into, can calculate the production pressure difference Δp w , normalized pressure Δp w / q w , cumulative output G q , material balance time t mb , pressure response function derivative Logarithmic time derivative of the pressure response function and the pressure response function p u ; Draw tp u and Plate, preliminarily identify possible characteristic flow segments through plate morphology.

6. According to the oil and gas well testing analysis method based on production data of claim 1, the specific manner of establishing the accurate identification model of the characteristic flow segment based on the normalized pressure equation is: Under fixed production conditions, the pressure response function p of the characteristic flow section at time t u (t) is a linear function of the power function of the time variable t, expressed as: p u (t)=a u +b u ·t n ; In the formula, a u and b u is the characteristic flow constant; n is the characteristic flow index, and t is the time variable; When the flow is radial, the pressure response function p of the characteristic flow section at time t is u (t) is a linear function of the power function of the time variable t, expressed as: p u (t)=a u +b u ·lnt The logarithmic time derivative of the characteristic flow segment is but: In the double logarithmic coordinate system, the straight line with a slope n of the logarithmic time derivative is the characteristic flow segment; when the output changes relatively slowly, the t of the characteristic flow segment when the output changes mbe The -RNP equation is: RNP(t)=a u +b u ·t mbe ; In the formula, RNP ( t ) is the normalized pressure at time t; t mbe is the effective material balance time; in:

7. The oil and gas well testing analysis method based on production data according to claim 6, characterized in that: The method for accurately identifying the characteristic flow segment of long-term production data according to the accurate identification model is as follows: According to the initially identified characteristic flow segment, the t mbe -RNP equation; The characteristic flow constant a of the characteristic flow section is calculated using the moving window method u and b u vs. time variable t, and view t mbe -RNP equation straight line segment and characteristic flow parameter a u and b u The constant segment of the curve with time variable t is used to determine whether a specified characteristic flow segment occurs and the duration range of the characteristic flow segment (t min ,t max ); In the time range (t min ,t max ) through t mbe -RNP equation straight line segment linear fitting to obtain characteristic flow parameter a u and b u , calculate the pressure response function and logarithmic time derivative of the characteristic flow section.

8. The oil and gas well testing analysis method based on production data according to claim 7, characterized in that: The pressure response function and logarithmic time derivative of the characteristic flow section are:

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